Contributing¶

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

The project is hosted on http://github.com/scikit-learn/scikit-learn

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

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

On Unix you can simply type make in the top-level folder to build in-place and launch all the tests. Have a look at the Makefile for additional utilities.

Contributing code¶

How to contribute¶

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

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

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.
Clone this copy to your local disk (you need the git program to do this):
$ git clone git@github.com:YourLogin/scikit-learn.git
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.

If your changes are not just trivial fixes, it is better to directly work in a branch with the name of the feature your are working on. In this case, replace step 4 by step 5:

Create a branch to host your changes and publish it on your public repo:
$ git checkout -b my-feature
$ gid add modified_files
$ git commit
$ git push origin my-feature

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, please check that your contribution complies with the following rules:

Follow the coding-guidelines (see below).

All public methods should have informative docstrings with sample usage presented as doctests when appropriate.
Code with a good unittest coverage (at least 80%), check with:
$ pip install nose coverage
$ nosetests --with-coverage path/to/tests_for_package
All other tests pass when everything is rebuilt from scrath, under Unix, check with (from the toplevel source folder):
$ make
No pyflakes warnings, check with:
$ pip install pyflakes
$ pyflakes path/to/module.py
No PEP8 warnings, check with:
$ pip install pep8
$ pep8 path/to/module.py
At least one example script in the examples/ folder. Have a look at other examples for reference. Example should demonstrate why this method is useful in practice and if possible compare it to other methods available in the scikit.

At least one paragraph of narrative documentation with links to references in the literature (with PDF links when possible) and the example.

The documentation should also include expected time and space complexity of the algorithm and scalablity, e.g. “this algorithm can scale to a large number of samples > 100000, but does not scale in dimensionality: n_features is expected to be lower than 100”.

To build the documentation see documentation below.

Bonus points for contributions that include a performance analysis with a benchmark script and profiling output (please report on the mailing list or on the github wiki).

Also check out the following guide on How to optimize for speed for more details on profiling and cython optimizations.

Note

The current state of the scikit-learn code base is not compliant with all of those guidelines but we expect that enforcing those constraints on all new contributions will get the overall code base quality in the right direction.

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.

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.

For building the documentation, you will need sphinx and matplotlib.

Warning

Sphinx version

While we do our best to have the documentation build under as many version of Sphinx as possible, the different versions tend to behave slightly differently. To get the best results, you should use version 1.0.

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 formatted code makes it easier to share code ownership. The scikit learn tries to follow closely the official 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 recommendations. It makes the code harder to read as the origin of symbols is no longer explicitly referenced, but most important, it prevents using a static analysis tool like pyflakes to automatically find bugs in scikit.

Use the numpy docstring standard in all your docstrings.

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 supervised 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() ?

Working notes¶

For unresolved issues, TODOs, remarks on ongoing work, developers are adviced to maintain notes on the github wiki: https://github.com/scikit-learn/scikit-learn/wiki

Specific models¶

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

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