Upcoming events

Sprint planning: 1 April 2011

Places

• In Paris: at Logilab's (104 boulevard blanqui, Paris)
• In Boston at MIT
• On IRC (#scikit-learn on irc.freenode.net)

People present

Please add skills/interests or planned task, to facilitate the sprint organization and pairing of people on tasks. To share knowledge as much as possible, it would be ideal to have pair-like programming of 2 people on a task, with different skills.

• Gaël Varoquaux: task: code review, pair programming on specific task where needed.
• Julien Miotte
• Feth Arezki
• Nelle Varoquaux

Tasks

In addition to the tasks listed below, it is useful to consider any issue in this list : https://github.com/scikit-learn/scikit-learn/issues

Easy

• Improve test coverage: Run 'make test-coverage' after installing the coverage module, find low hanging fruits to improve coverage, and add tests. Try to test the logic, and not simple aim for augmenting the number of lines covered.

Not requiring expertise in machine learning

• Logging: create a logger (using the standard libary's 'logging' module) for the scikit learn and a couple of simple print functions to replace 'print calls' through out the scikit. Talk to Gael Varoquaux about this task.
• Prettify the PDF documentation - for instance modify the LaTeX stylesheet so that blocks are less ugly. Talk to Gael Varoquaux about this task.
• Multiple figures in documentation examples: when generating the documentation, figures plotted via matplotlib are captured using the code in doc/sphinxext/gen_rst.py. However, currently only the first figure is captured. It would be nice to capture all the figures.
• Restore the 'source' link on the documentation: the html template does not give a 'source' link to the rst source of the file. This should be added back.

Branch merging

A lot of good work is waiting for small fixes in branches:

• merge Hierarchical Clustering (merge in the HCluster v2 pull request)
• merge LDA improvements

Machine learning tasks

• Improve the documentation: You understand some aspects machine-learning. You can help making the scikit rock without writing a line of code: http://scikit-learn.sourceforge.net/developers/index.html#documentation
• Matrix factorization (Sparse PCA, NNMF)
• Add transform to LDA + pipe LDA with covariance estimator

More ambitious/long term tasks

• Random Forest
• Fused Lasso
• Group Lasso
• MultiTask Lasso
• KMeans with triangular inequality
• Manifold learning
• Bayesian classification (e.g. RVM)

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Past sprints