Contributing

This project is a community effort, and everyone is welcomed to contribute.

Submitting a bug report

In case you experience difficulties using the package, do not hesitate to submit a ticket to the Bug Tracker.

You are also welcomed to post there feature requests and patches.

Retrieving the latest code

You can check the latest sources with the command:

git clone git://github.com/scikit-learn/scikit-learn.git

or if you have write privileges:

git clone git@github.com:scikit-learn/scikit-learn.git

You can also check out the sources online in the web page http://github.com/scikit-learn/scikit-learn

If you run the development version, it is cumbersome to re-install the package each time you update the sources. It is thus preferred that you add the scikit-directory to your PYTHONPATH and build the extension in place:

python setup.py build_ext --inplace

Contributing code

How to contribute

The prefered way to contribute to scikit-learn is to fork the main repository on github:

1. Create an account on github if you don’t have one already.

2. Fork the scikit-learn repo: click on the ‘Fork’ button, at the top, center of the page. This creates a copy of the code on the github server where you can work.

3. Clone this copy to your local disk (you need the git program to do this):

   $ git clone git@github.com:YourLogin/scikit-learn.git

4. Work on this copy, on your computer, using git to do the version control:

   $ git add modified_files
   $ git commit
   $ git push origin master

   and so on.

When you are ready, and you have pushed your changes on your github repo, go the web page of the repo, and click on ‘Pull request’ to send us a pull request. Send us a mail with your pull request, and we can look at your changes, and integrate them.

Before asking for a pull or a review, be sure to read the coding-guidelines (below).

Also, make sure that your code is tested, and that all the tests for the scikit pass.

EasyFix Issues

The best way to get your feet wet is to pick up an issue from the issue tracker that are labeled as EasyFix. This means that the knowledge needed to solve the issue is low, but still you are helping the project and letting more experienced developers concentrate on other issues.

Roadmap

Here you will find a detailed roadmap, with a description on what’s planned to be implemented in the following releases.

Documentation

We are glad to accept any sort of documentation: function docstrings, rst docs (like this one), tutorials, etc. Rst docs live in the source code repository, under directory doc/.

You can edit them using any text editor and generate the html docs by typing from the doc/ directory make html (or make html-noplot, see README in that directory for more info). That should create a directory _build/html/ with html files that are viewable in a web browser.

Developers web site

More information can be found at the developer’s wiki.

Other ways to contribute

Code is not the only way to contribute to this project. For instance, documentation is also a very important part of the project and ofter doesn’t get as much attention as it deserves. If you find a typo in the documentation, or have made improvements, don’t hesitate to send an email to the mailing list or a github pull request. Full documentation can be found under directory doc/.

It also helps us if you spread the word: reference it from your blog, articles, link to us from your website, or simply by saying “I use it”:

Coding guidelines

The following are some guidelines on how new code should be written. Of course, there are special cases and there will be exceptions to these rules. However, following these rules when submitting new code makes the review easier so new code can be integrated in less time.

Uniformly formated code makes it easier to share code ownership. The scikit learn tries to follow closely the officiel Python guidelines detailed in PEP8 that details how code should be formatted, and indented. Please read it and follow it.

In addition, we add the following guidelines:

• Use underscores to separate words in non class names: n_samples rather than nsamples.
• Avoid multiple statements on one line. Prefer a line return after a control flow statement (if/for).
• Use relative imports for references inside scikits.learn.
• Please don’t use `import *` in any case. It is considered harmful by the official Python recommandations. It makes the code harder to read as the origin of symbols is no longer explicitely referenced, but most important, it prevents using a static analysis tool like pyflakes to automatically find bugs in the scikit.

A good example of code that we like can be found here.

APIs of scikit learn objects

To have a uniform API, we try to have a common basic API for all the objects. In addition, to avoid the proliferation of framework code, we try to adopt simple conventions and limit to a minimum the number of methods an object has to implement.

Different objects

The main objects of the scikit learn are (one class can implement multiple interfaces):

Estimator:

The base object, implements:

estimator = obj.fit(data)

Predictor:

For suppervised learning, or some unsupervised problems, implements:

prediction = obj.predict(data)

Transformer:

For filtering or modifying the data, in a supervised or unsupervised way, implements:

new_data = obj.transform(data)

When fitting and transforming can be performed much more efficiently together than separately, implements:

new_data = obj.fit_transform(data)

Model:

A model that can give a goodness of fit or a likelihood of unseen data, implements (higher is better):

score = obj.score(data)

Estimators

The API has one predominant object: the estimator. A estimator is an object that fits a model based on some training data and is capable of inferring some properties on new data. It can be for instance a classifier or a regressor. All estimators implement the fit method:

estimator.fit(X, y)

Instantiation

This concerns the object creation. The object’s __init__ method might accept as arguments constants that determine the estimator behavior (like the C constant in SVMs).

It should not, however, take the actual training data as argument, as this is left to the fit() method:

clf2 = SVC(C=2.3)
clf3 = SVC([[1, 2], [2, 3]], [-1, 1]) # WRONG!

The arguments that go in the __init__ should all be keyword arguments with a default value. In other words, a user should be able to instanciate an estimator without passing to it any arguments.

The arguments in given at instanciation of an estimator should all correspond to hyper parameters describing the model or the optimisation problem that estimator tries to solve. They should however not be parameters of the estimation routine: these are passed directly to the fit method.

In addition, every keyword argument given to the `__init__` should correspond to an attribute on the instance. The scikit relies on this to find what are the relevent attributes to set on an estimator when doing model selection.

All estimators should inherit from scikit.learn.base.BaseEstimator

Fitting

The next thing you’ll probably want to do is to estimate some parameters in the model. This is implemented in the .fit() method.

The fit method takes as argument the training data, which can be one array in the case of unsupervised learning, or two arrays in the case of supervised learning.

Note that the model is fitted using X and y but the object holds no reference to X, y. There are however some exceptions to this, as in the case of precomputed kernels where you need to store access these data in the predict method.

Parameters

• X : array-like, with shape = [N, D], where N is the number of samples and D is the number of features.
• Y : array, with shape = [N], where N is the number of samples.
• args, kwargs. Parameters can also be set in the fit method.

X.shape[0] should be the same as Y.shape[0]. If this requisite is not met, an exception should be raised.

Y might be dropped in the case of unsupervised learning.

The method should return the object (self).

Python tuples

In addition to numpy arrays, all methods should be able to accept python tuples as arguments. In practice, this means you should call numpy.asanyarray at the beginning at each public method that accepts arrays.

Optional Arguments

In iterative algorithms, number of iterations should be specified by an int called n_iter.

Unresolved API issues

Some things are must still be decided:

• what should happen when predict is called before than fit() ?
• which exception should be raised when arrays’ shape do not match in fit() ?

Specific models

In linear models, coefficients are stored in an array called coef_, and independent term is stored in intercept_.