This project is a community effort, and everyone is welcome to contribute.
The project is hosted on http://github.com/scikit-learn/scikit-learn
1.1. Submitting a bug report¶
In case you experience issues using this package, do not hesitate to submit a ticket to the Bug Tracker. You are also welcome to post feature requests or links to pull requests.
1.2. Retrieving the latest code¶
You can check out the latest sources with the command:
git clone git://github.com/scikit-learn/scikit-learn.git
or if you have write privileges:
git clone email@example.com:scikit-learn/scikit-learn.git
If you run the development version, it is cumbersome to reinstall the package each time you update the sources. It is thus preferred that you add the scikit-learn directory to your PYTHONPATH and build the extension in place:
python setup.py build_ext --inplace
On Unix-like systems, 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.
1.3. Contributing code¶
To avoid duplicating work, it is highly advised that you contact the developers on the mailing list before starting work on a non-trivial feature.
1.3.1. How to contribute¶
The preferred way to contribute to scikit-learn is to fork the main repository on GitHub:
Create an account on GitHub if you do not already have one.
Fork the project repository: click on the ‘Fork’ button near the top of the page. This creates a copy of the code under your account on the GitHub server.
Clone this copy to your local disk:$ git clone firstname.lastname@example.org:YourLogin/scikit-learn.git
Create a branch to hold your changes:$ git checkout -b my-feature
and start making changes. Never work in the master branch!
Work on this copy, on your computer, using Git to do the version control. When you’re done editing, do:$ git add modified_files $ git commit
to record your changes in Git, then push them to GitHub with:$ git push -u origin my-feature
Finally, go to the web page of the your fork of the scikit-learn repo, and click ‘Pull request’ to send your changes to the maintainers for review. request. This will send an email to the committers, but might also send an email to the mailing list in order to get more visibility.
In the above setup, your origin remote repository points to YourLogin/scikit-learn.git. If you wish to fetch/merge from the main repository instead of your forked one, you will need to add another remote to use instead of origin. If we choose the name upstream for it, the command will be:
$ git remote add upstream https://github.com/scikit-learn/scikit-learn.git
(If any of the above seems like magic to you, then look up the Git documentation on the web.)
It is recommended to check that your contribution complies with the following rules before submitting a pull request:
Follow the coding-guidelines (see below).
When applicable, use the Validation tools and other code in the sklearn.utils submodule. A list of utility routines available for developers can be found in the Utilities for Developers page.
All public methods should have informative docstrings with sample usage presented as doctests when appropriate.
All other tests pass when everything is rebuilt from scratch. On Unix-like systems, check with (from the toplevel source folder):$ make
When adding additional functionality, provide at least one example script in the examples/ folder. Have a look at other examples for reference. Examples should demonstrate why the new functionality is useful in practice and, if possible, compare it to other methods available in scikit-learn.
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 scalability, 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 the documentation section below.
You can also check for common programming errors with the following tools:
Code with a good unittest coverage (at least 90%, better 100%), check with:$ pip install nose coverage $ nosetests --with-coverage path/to/tests_for_package
see also Testing and improving test coverage
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
AutoPEP8 can help you fix some of the easy redundant errors:$ pip install autopep8 $ autopep8 path/to/pep8.py
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 How to optimize for speed guide for more details on profiling and Cython optimizations.
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.
1.3.2. Easy Issues¶
A great way to start contributing to scikit-learn is to pick an item from the list of Easy issues in the issue tracker. Resolving these issues allow you to start contributing to the project without much prior knowledge. Your assistance in this area will be greatly appreciated by the more experienced developers as it helps free up their time to concentrate on other issues.
We are glad to accept any sort of documentation: function docstrings, reStructuredText documents (like this one), tutorials, etc. reStructuredText documents live in the source code repository under the doc/ directory.
You can edit the documentation using any text editor, and then generate the HTML output by typing make html from the doc/ directory. Alternatively, make html-noplot can be used to quickly generate the documentation without the example gallery. The resulting HTML files will be placed in _build/html/ and are viewable in a web browser. See the README file in the doc/ directory for more information.
When you are writing documentation, it is important to keep a good compromise between mathematical and algorithmic details, and give intuition to the reader on what the algorithm does. It is best to always start with a small paragraph with a hand-waiving explanation of what the method does to the data and a figure (coming from an example) illustrating it.
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.
1.3.4. Testing and improving test coverage¶
High-quality unit testing is a corner-stone of the sciki-learn development process. For this purpose, we use the nose package. The tests are functions appropriately names, located in tests subdirectories, that check the validity of the algorithms and the different options of the code.
The full scikit-learn tests can be run using ‘make’ in the root folder. Alternatively, running ‘nosetests’ in a folder will run all the tests of the corresponding subpackages.
We expect code coverage of new features to be at least around 90%.
Workflow to improve test coverage
To test code coverage, you need to install the coverage package in addition to nose.
- Run ‘make test-coverage’. The output lists for each file the line numbers that are not tested.
- Find a low hanging fruit, looking at which lines are not tested, write or adapt a test specifically for these lines.
1.4. Other ways to contribute¶
Code is not the only way to contribute to scikit-learn. For instance, documentation is also a very important part of the project and often doesn’t get as much attention as it deserves. If you find a typo in the documentation, or have made improvements, do not hesitate to send an email to the mailing list or submit a GitHub pull request. Full documentation can be found under the doc/ directory.
It also helps us if you spread the word: reference the project from your blog and articles, link to it from your website, or simply say “I use it”:
1.5. 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 project tries to closely follow the official Python guidelines detailed in PEP8 that detail 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 scikit-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-learn.
- Use the numpy docstring standard in all your docstrings.
A good example of code that we like can be found here.
1.5.1. Input validation¶
The module sklearn.utils contains various functions for doing input validation and conversion. Sometimes, np.asarray suffices for validation; do not use np.asanyarray or np.atleast_2d, since those let NumPy’s np.matrix through, which has a different API (e.g., * means dot product on np.matrix, but Hadamard product on np.ndarray).
In other cases, be sure to call safe_asarray, atleast2d_or_csr, as_float_array or array2d on any array-like argument passed to a scikit-learn API function. The exact function to use depends mainly on whether scipy.sparse matrices must be accepted.
For more information, refer to the Utilities for Developers page.
1.5.2. Random Numbers¶
If your code depends on a random number generator, do not use numpy.random.random() or similar routines. To ensure repeatability in error checking, the routine should accept a keyword random_state and use this to construct a numpy.random.RandomState object. See sklearn.utils.check_random_state in Utilities for Developers.
Here’s a simple example of code using some of the above guidelines:
from sklearn.utils import array2d, check_random_state def choose_random_sample(X, random_state=0): """ Choose a random point from X Parameters ---------- X : array-like, shape = (n_samples, n_features) array representing the data random_state : RandomState or an int seed (0 by default) A random number generator instance to define the state of the random permutations generator. Returns ------- x : numpy array, shape = (n_features,) A random point selected from X """ X = array2d(X) random_state = check_random_state(random_state) i = random_state.randint(X.shape) return X[i]
1.6. 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 must implement.
1.6.1. Different objects¶
The main objects in scikit-learn are (one class can implement multiple interfaces):
The base object, implements:
estimator = obj.fit(data)
For supervised learning, or some unsupervised problems, implements:
prediction = obj.predict(data)
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)
A model that can give a goodness of fit measure or a likelihood of unseen data, implements (higher is better):
score = obj.score(data)
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:
All built-in estimators also have a set_params method, which sets data-independent parameters (overriding previous parameter values passed to __init__). This method is not required for an object to be an estimator.
All estimators should inherit from sklearn.base.BaseEstimator.
This concerns the creation of an object. The object’s __init__ method might accept constants as arguments that determine the estimator’s behavior (like the C constant in SVMs). It should not, however, take the actual training data as an 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 accepted by __init__ should all be keyword arguments with a default value. In other words, a user should be able to instantiate an estimator without passing any arguments to it. The arguments should all correspond to hyperparameters describing the model or the optimisation problem the estimator tries to solve. These initial arguments (or parameters) are always remembered by the estimator. Also note that they should not be documented under the Attributes section, but rather under the Parameters section for that estimator.
In addition, every keyword argument accepted by ``__init__`` should correspond to an attribute on the instance. Scikit-learn relies on this to find the relevant attributes to set on an estimator when doing model selection.
To summarize, a __init__ should look like:
def __init__(self, param1=1, param2=2): self.param1 = param1 self.param2 = param2
There should be no logic, and the parameters should not be changed. The corresponding logic should be put where the parameters are used. The following is wrong:
def __init__(self, param1=1, param2=2, param3=3): # WRONG: parameters should not be modified if param1 > 1: param2 += 1 self.param1 = param1 # WRONG: the object's attributes should have exactly the name of # the argument in the constructor self.param3 = param2
Scikit-learn relies on this mechanism to introspect objects to set their parameters by cross-validation.
The next thing you will probably want to do is to estimate some parameters in the model. This is implemented in the fit() method.
The fit() method takes the training data as arguments, 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 and y. There are, however, some exceptions to this, as in the case of precomputed kernels where this data must be stored for use by the predict method.
|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.|
|kwargs||optional data-dependent parameters.|
X.shape should be the same as y.shape. If this requisite is not met, an exception of type ValueError should be raised.
y might be ignored in the case of unsupervised learning. However, to make it possible to use the estimator as part of a pipeline that can mix both supervised and unsupervised transformers, even unsupervised estimators are kindly asked to accept a y=None keyword argument in the second position that is just ignored by the estimator.
The method should return the object (self). This pattern is useful to be able to implement quick one liners in an IPython session such as:
y_predicted = SVC(C=100).fit(X_train, y_train).predict(X_test)
Depending on the nature of the algorithm, fit can sometimes also accept additional keywords arguments. However, any parameter that can have a value assigned prior to having access to the data should be an __init__ keyword argument. fit parameters should be restricted to directly data dependent variables. For instance a Gram matrix or an affinity matrix which are precomputed from the data matrix X are data dependent. A tolerance stopping criterion tol is not directly data dependent (although the optimal value according to some scoring function probably is).
22.214.171.124. Estimated Attributes¶
Attributes that have been estimated from the data must always have a name ending with trailing underscore, for example the coefficients of some regression estimator would be stored in a coef_ attribute after fit() has been called.
The last-mentioned attributes are expected to be overridden when you call fit a second time without taking any previous value into account: fit should be idempotent.
126.96.36.199. Optional Arguments¶
In iterative algorithms, the number of iterations should be specified by an integer called n_iter.
1.6.3. Unresolved API issues¶
Some things are must still be decided:
- what should happen when predict is called before fit() ?
- which exception should be raised when the shape of arrays do not match in fit() ?
1.6.4. Working notes¶
For unresolved issues, TODOs, and remarks on ongoing work, developers are advised to maintain notes on the GitHub wiki.
1.6.5. Specific models¶
In linear models, coefficients are stored in an array called coef_, and the independent term is stored in intercept_.