spaCy/CONTRIBUTING.md

Contribute to spaCy

Thanks for your interest in contributing to spaCy 🎉 The project is maintained by @honnibal and @ines, and we'll do our best to help you get started. This page will give you a quick overview of how things are organized and most importantly, how to get involved.

Table of contents

1. Issues and bug reports
2. Contributing to the code base
3. Code conventions
4. Adding tests
5. Updating the website
6. Publishing extensions and plugins
7. Code of conduct

Issues and bug reports

First, do a quick search to see if the issue has already been reported. If so, it's often better to just leave a comment on an existing issue, rather than creating a new one. Old issues also often include helpful tips and solutions to common problems. You should also check the troubleshooting guide to see if your problem is already listed there.

If you're looking for help with your code, consider posting a question on Stack Overflow instead. If you tag it spacy and python, more people will see it and hopefully be able to help. Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's shared publicly, so that more people can benefit from it.

Submitting issues

When opening an issue, use a descriptive title and include your environment (operating system, Python version, spaCy version). Our issue template helps you remember the most important details to include. If you've discovered a bug, you can also submit a regression test straight away. When you're opening an issue to report the bug, simply refer to your pull request in the issue body. A few more tips:

• Describing your issue: Try to provide as many details as possible. What exactly goes wrong? How is it failing? Is there an error? "XY doesn't work" usually isn't that helpful for tracking down problems. Always remember to include the code you ran and if possible, extract only the relevant parts and don't just dump your entire script. This will make it easier for us to reproduce the error.

• Getting info about your spaCy installation and environment: If you're using spaCy v1.7+, you can use the command line interface to print details and even format them as Markdown to copy-paste into GitHub issues: python -m spacy info --markdown.

• Checking the model compatibility: If you're having problems with a statistical model, it may be because the model is incompatible with your spaCy installation. In spaCy v2.0+, you can check this on the command line by running python -m spacy validate.

• Sharing a model's output, like dependencies and entities: spaCy v2.0+ comes with built-in visualizers that you can run from within your script or a Jupyter notebook. For some issues, it's helpful to include a screenshot of the visualization. You can simply drag and drop the image into GitHub's editor and it will be uploaded and included.

• Sharing long blocks of code or logs: If you need to include long code, logs or tracebacks, you can wrap them in <details> and </details>. This collapses the content so it only becomes visible on click, making the issue easier to read and follow.

Issue labels

See this page for an overview of the system we use to tag our issues and pull requests.

Contributing to the code base

You don't have to be an NLP expert or Python pro to contribute, and we're happy to help you get started. If you're new to spaCy, a good place to start is the spaCy 101 guide and the help wanted (easy) label, which we use to tag bugs and feature requests that are easy and self-contained. If you've decided to take on one of these problems and you're making good progress, don't forget to add a quick comment to the issue. You can also use the issue to ask questions, or share your work in progress.

What belongs in spaCy?

Every library has a different inclusion philosophy — a policy of what should be shipped in the core library, and what could be provided in other packages. Our philosophy is to prefer a smaller core library. We generally ask the following questions:

• What would this feature look like if implemented in a separate package? Some features would be very difficult to implement externally – for example, changes to spaCy's built-in methods. In contrast, a library of word alignment functions could easily live as a separate package that depended on spaCy — there's little difference between writing import word_aligner and import spacy.word_aligner. spaCy v2.0+ makes it easy to implement custom pipeline components, and add your own attributes, properties and methods to the Doc, Token and Span. If you're looking to implement a new spaCy feature, starting with a custom component package is usually the best strategy. You won't have to worry about spaCy's internals and you can test your module in an isolated environment. And if it works well, we can always integrate it into the core library later.

• Would the feature be easier to implement if it relied on "heavy" dependencies spaCy doesn't currently require? Python has a very rich ecosystem. Libraries like scikit-learn, SciPy, Gensim or TensorFlow/Keras do lots of useful things — but we don't want to have them as dependencies. If the feature requires functionality in one of these libraries, it's probably better to break it out into a different package.

• Is the feature orthogonal to the current spaCy functionality, or overlapping? spaCy strongly prefers to avoid having 6 different ways of doing the same thing. As better techniques are developed, we prefer to drop support for "the old way". However, it's rare that one approach entirely dominates another. It's very common that there's still a use-case for the "obsolete" approach. For instance, WordNet is still very useful — but word vectors are better for most use-cases, and the two approaches to lexical semantics do a lot of the same things. spaCy therefore only supports word vectors, and support for WordNet is currently left for other packages.

• Do you need the feature to get basic things done? We do want spaCy to be at least somewhat self-contained. If we keep needing some feature in our recipes, that does provide some argument for bringing it "in house".

Getting started

To make changes to spaCy's code base, you need to fork then clone the GitHub repository and build spaCy from source. You'll need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, virtualenv and git installed. The compiler is usually the trickiest part.

python -m pip install -U pip
git clone https://github.com/explosion/spaCy
cd spaCy

python -m venv .env
source .env/bin/activate
export PYTHONPATH=`pwd`
pip install -r requirements.txt
python setup.py build_ext --inplace

If you've made changes to .pyx files, you need to recompile spaCy before you can test your changes by re-running python setup.py build_ext --inplace. Changes to .py files will be effective immediately.

📖 For more details and instructions, see the documentation on compiling spaCy from source and the quickstart widget to get the right commands for your platform and Python version.

Contributor agreement

If you've made a contribution to spaCy, you should fill in the spaCy contributor agreement to ensure that your contribution can be used across the project. If you agree to be bound by the terms of the agreement, fill in the template and include it with your pull request, or submit it separately to .github/contributors/. The name of the file should be your GitHub username, with the extension .md. For example, the user example_user would create the file .github/contributors/example_user.md.

Fixing bugs

When fixing a bug, first create an issue if one does not already exist. The description text can be very short – we don't want to make this too bureaucratic.

Next, create a test file named test_issue[ISSUE NUMBER].py in the spacy/tests/regression folder. Test for the bug you're fixing, and make sure the test fails. Next, add and commit your test file referencing the issue number in the commit message. Finally, fix the bug, make sure your test passes and reference the issue in your commit message.

📖 For more information on how to add tests, check out the tests README.

Code conventions

Code should loosely follow pep8. As of v2.1.0, spaCy uses black for code formatting and flake8 for linting its Python modules. If you've built spaCy from source, you'll already have both tools installed.

⚠️ Note that formatting and linting is currently only possible for Python modules in .py files, not Cython modules in .pyx and .pxd files.

Code formatting

black is an opinionated Python code formatter, optimized to produce readable code and small diffs. You can run black from the command-line, or via your code editor. For example, if you're using Visual Studio Code, you can add the following to your settings.json to use black for formatting and auto-format your files on save:

{
  "python.formatting.provider": "black",
  "[python]": {
    "editor.formatOnSave": true
  }
}

See here for the full list of available editor integrations.

Disabling formatting

There are a few cases where auto-formatting doesn't improve readability – for example, in some of the language data files like the tag_map.py, or in the tests that construct Doc objects from lists of words and other labels. Wrapping a block in # fmt: off and # fmt: on lets you disable formatting for that particular code. Here's an example:

# fmt: off
text = "I look forward to using Thingamajig.  I've been told it will make my life easier..."
heads = [1, 1, 1, 1, 3, 4, 1, 6, 11, 11, 11, 11, 14, 14, 11, 16, 17, 14, 11]
deps = ["nsubj", "ROOT", "advmod", "prep", "pcomp", "dobj", "punct", "",
        "nsubjpass", "aux", "auxpass", "ROOT", "nsubj", "aux", "ccomp",
        "poss", "nsubj", "ccomp", "punct"]
# fmt: on

Code linting

flake8 is a tool for enforcing code style. It scans one or more files and outputs errors and warnings. This feedback can help you stick to general standards and conventions, and can be very useful for spotting potential mistakes and inconsistencies in your code. The most important things to watch out for are syntax errors and undefined names, but you also want to keep an eye on unused declared variables or repeated (i.e. overwritten) dictionary keys. If your code was formatted with black (see above), you shouldn't see any formatting-related warnings.

The .flake8 config defines the configuration we use for this codebase. For example, we're not super strict about the line length, and we're excluding very large files like lemmatization and tokenizer exception tables.

Ideally, running the following command from within the repo directory should not return any errors or warnings:

flake8 spacy

Disabling linting

Sometimes, you explicitly want to write code that's not compatible with our rules. For example, a module's __init__.py might import a function so other modules can import it from there, but flake8 will complain about an unused import. And although it's generally discouraged, there might be cases where it makes sense to use a bare except.

To ignore a given line, you can add a comment like # noqa: F401, specifying the code of the error or warning we want to ignore. It's also possible to ignore several comma-separated codes at once, e.g. # noqa: E731,E123. Here are some examples:

# The imported class isn't used in this file, but imported here, so it can be
# imported *from* here by another module.
from .submodule import SomeClass  # noqa: F401

try:
    do_something()
except:  # noqa: E722
    # This bare except is justified, for some specific reason
    do_something_else()

Python conventions

All Python code must be written compatible with Python 3.6+. Code that interacts with the file-system should accept objects that follow the pathlib.Path API, without assuming that the object inherits from pathlib.Path. If the function is user-facing and takes a path as an argument, it should check whether the path is provided as a string. Strings should be converted to pathlib.Path objects. Serialization and deserialization functions should always accept file-like objects, as it makes the library IO-agnostic. Working on buffers makes the code more general, easier to test, and compatible with Python 3's asynchronous IO.

Although spaCy uses a lot of classes, inheritance is viewed with some suspicion — it's seen as a mechanism of last resort. You should discuss plans to extend the class hierarchy before implementing.

We have a number of conventions around variable naming that are still being documented, and aren't 100% strict. A general policy is that instances of the class Doc should by default be called doc, Token token, Lexeme lex, Vocab vocab and Language nlp. You should avoid naming variables that are of other types these names. For instance, don't name a text string doc — you should usually call this text. Two general code style preferences further help with naming. First, lean away from introducing temporary variables, as these clutter your namespace. This is one reason why comprehension expressions are often preferred. Second, keep your functions shortish, so they can work in a smaller scope. Of course, this is a question of trade-offs.

Cython conventions

spaCy's core data structures are implemented as Cython cdef classes. Memory is managed through the cymem.cymem.Pool class, which allows you to allocate memory which will be freed when the Pool object is garbage collected. This means you usually don't have to worry about freeing memory. You just have to decide which Python object owns the memory, and make it own the Pool. When that object goes out of scope, the memory will be freed. You do have to take care that no pointers outlive the object that owns them — but this is generally quite easy.

All Cython modules should have the # cython: infer_types=True compiler directive at the top of the file. This makes the code much cleaner, as it avoids the need for many type declarations. If possible, you should prefer to declare your functions nogil, even if you don't especially care about multi-threading. The reason is that nogil functions help the Cython compiler reason about your code quite a lot — you're telling the compiler that no Python dynamics are possible. This lets many errors be raised, and ensures your function will run at C speed.

Cython gives you many choices of sequences: you could have a Python list, a numpy array, a memory view, a C++ vector, or a pointer. Pointers are preferred, because they are fastest, have the most explicit semantics, and let the compiler check your code more strictly. C++ vectors are also great — but you should only use them internally in functions. It's less friendly to accept a vector as an argument, because that asks the user to do much more work.

Here's how to get a pointer from a numpy array, memory view or vector:

cdef void get_pointers(np.ndarray[int, mode='c'] numpy_array, vector[int] cpp_vector, int[::1] memory_view) nogil:
    pointer1 = <int*>numpy_array.data
    pointer2 = cpp_vector.data()
    pointer3 = &memory_view[0]

Both C arrays and C++ vectors reassure the compiler that no Python operations are possible on your variable. This is a big advantage: it lets the Cython compiler raise many more errors for you.

When getting a pointer from a numpy array or memoryview, take care that the data is actually stored in C-contiguous order — otherwise you'll get a pointer to nonsense. The type-declarations in the code above should generate runtime errors if buffers with incorrect memory layouts are passed in.

To iterate over the array, the following style is preferred:

cdef int c_total(const int* int_array, int length) nogil:
    total = 0
    for item in int_array[:length]:
        total += item
    return total

If this is confusing, consider that the compiler couldn't deal with for item in int_array: — there's no length attached to a raw pointer, so how could we figure out where to stop? The length is provided in the slice notation as a solution to this. Note that we don't have to declare the type of item in the code above — the compiler can easily infer it. This gives us tidy code that looks quite like Python, but is exactly as fast as C — because we've made sure the compilation to C is trivial.

Your functions cannot be declared nogil if they need to create Python objects or call Python functions. This is perfectly okay — you shouldn't torture your code just to get nogil functions. However, if your function isn't nogil, you should compile your module with cython -a --cplus my_module.pyx and open the resulting my_module.html file in a browser. This will let you see how Cython is compiling your code. Calls into the Python run-time will be in bright yellow. This lets you easily see whether Cython is able to correctly type your code, or whether there are unexpected problems.

Finally, if you're new to Cython, you should expect to find the first steps a bit frustrating. It's a very large language, since it's essentially a superset of Python and C++, with additional complexity and syntax from numpy. The documentation isn't great, and there are many "traps for new players". Working in Cython is very rewarding once you're over the initial learning curve. As with C and C++, the first way you write something in Cython will often be the performance-optimal approach. In contrast, Python optimization generally requires a lot of experimentation. Is it faster to have an if item in my_dict check, or to use .get()? What about try/except? Does this numpy operation create a copy? There's no way to guess the answers to these questions, and you'll usually be dissatisfied with your results — so there's no way to know when to stop this process. In the worst case, you'll make a mess that invites the next reader to try their luck too. This is like one of those volcanic gas-traps, where the rescuers keep passing out from low oxygen, causing another rescuer to follow — only to succumb themselves. In short, just say no to optimizing your Python. If it's not fast enough the first time, just switch to Cython.

Resources to get you started

• PEP 8 Style Guide for Python Code (python.org)
• Official Cython documentation (cython.org)
• Writing C in Cython (explosion.ai)
• Multi-threading spaCy’s parser and named entity recognizer (explosion.ai)

Adding tests

spaCy uses the pytest framework for testing. For more info on this, see the pytest documentation. Tests for spaCy modules and classes live in their own directories of the same name. For example, tests for the Tokenizer can be found in /spacy/tests/tokenizer. To be interpreted and run, all test files and test functions need to be prefixed with test_.

When adding tests, make sure to use descriptive names, keep the code short and concise and only test for one behavior at a time. Try to parametrize test cases wherever possible, use our pre-defined fixtures for spaCy components and avoid unnecessary imports.

Extensive tests that take a long time should be marked with @pytest.mark.slow. Tests that require the model to be loaded should be marked with @pytest.mark.models. Loading the models is expensive and not necessary if you're not actually testing the model performance. If all you need is a Doc object with annotations like heads, POS tags or the dependency parse, you can use the Doc constructor to construct it manually.

📖 For more guidelines and information on how to add tests, check out the tests README.

Updating the website

For instructions on how to build and run the website locally see Setup and installation in the website directory's README.

The docs can always use another example or more detail, and they should always be up to date and not misleading. To quickly find the correct file to edit, simply click on the "Suggest edits" button at the bottom of a page.

📖 For more info and troubleshooting guides, check out the website README.

Publishing spaCy extensions and plugins

We're very excited about all the new possibilities for community extensions and plugins in spaCy v2.0, and we can't wait to see what you build with it!

• An extension or plugin should add substantial functionality, be well-documented and open-source. It should be available for users to download and install as a Python package – for example via PyPi.

• Extensions that write to Doc, Token or Span attributes should be wrapped as pipeline components that users can add to their processing pipeline using nlp.add_pipe().

• When publishing your extension on GitHub, tag it with the topics spacy and spacy-extensions to make it easier to find. Those are also the topics we're linking to from the spaCy website. If you're sharing your project on Twitter, feel free to tag @spacy_io so we can check it out.

• Once your extension is published, you can open an issue on the issue tracker to suggest it for the resources directory on the website.

📖 For more tips and best practices, see the checklist for developing spaCy extensions.

Code of conduct

spaCy adheres to the Contributor Covenant Code of Conduct. By participating, you are expected to uphold this code.