We appreciate all kinds of help, so thank you!
First please read the overall project contributing guidelines. These are included in the Qiskit documentation here:
https://qiskit.org/documentation/contributing_to_qiskit.html
In addition to the general guidelines above there are specific details for contributing to Qiskit Machine Learning, these are documented below.
Code in Qiskit Machine Learning should conform to PEP8 and style/lint checks are run to validate this. Line length must be limited to no more than 100 characters. Docstrings should be written using the Google docstring format.
When submitting a pull request and you feel it is ready for review, please ensure that:
-
The code follows the code style of this project and successfully passes the unit tests. Machine Learning uses Pylint and PEP8 style guidelines.
You can run
make lint make style
from the root of the Machine Learning repository clone for lint and style conformance checks.
If your code fails the local style checks (specifically the black code formatting check) you can use
make blackto automatically fix update the code formatting.For unit testing please see Testing section below.
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The documentation has been updated accordingly. In particular, if a function or class has been modified during the PR, please update the docstring accordingly.
The documentation will be built/tested using Sphinx and should be free from errors and warnings.
You can run
make html
in the 'docs' folder. You might also like to check the html output to see the changes formatted output is as expected. You will find an index.html file in docs_build\html and you can navigate from there.
Please note that a spell check is run in CI, on the docstrings, since the text becomes part of the online API Documentation.
You can run
make spelllocally to check spelling though you would need to install pyenchant and be using hunspell-en-us as is used by the CI.For some words, such as names, technical terms, referring to parameters of the method etc., that are not in the en-us dictionary and get flagged as being misspelled, despite being correct, there is a .pylintdict custom word list file, in the root of the Machine Learning repo, where such words can be added, in alphabetic order, as needed.
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If it makes sense for your change that you have added new tests that cover the changes and any new function.
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Ensure that if your change has an end user facing impact (new feature, deprecation, removal etc) that you have added a reno release note for that change and that the PR is tagged for the changelog.
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Ensure all code, including unit tests, has the copyright header. The copyright date will be checked by CI build. The format of the date(s) is year of creation, last year changed. So for example:
# (C) Copyright IBM 2018, 2021.
If the year of creation is the same as last year changed then only one date is needed, for example:
# (C) Copyright IBM 2021.
If code is changed in a file make sure the copyright includes the current year. If there is just one date and it's a prior year then add the current year as the 2nd date, otherwise simply change the 2nd date to the current year. The year of creation date is never changed.
The changelog is automatically generated as part of the release process
automation. This works through a combination of the git log and the pull
request. When a release is tagged and pushed to github the release automation
bot looks at all commit messages from the git log for the release. It takes the
PR numbers from the git log (assuming a squash merge) and checks if that PR had
a Changelog: label on it. If there is a label it will add the git commit
message summary line from the git log for the release to the changelog.
If there are multiple Changelog: tags on a PR the git commit message summary
line from the git log will be used for each changelog category tagged.
The current categories for each label are as follows:
| PR Label | Changelog Category |
|---|---|
| Changelog: Deprecation | Deprecated |
| Changelog: New Feature | Added |
| Changelog: API Change | Changed |
| Changelog: Removal | Removed |
| Changelog: Bugfix | Fixed |
When making any end user facing changes in a contribution we have to make sure we document that when we release a new version of qiskit-machine-learning. The expectation is that if your code contribution has user facing changes that you will write the release documentation for these changes. This documentation must explain what was changed, why it was changed, and how users can either use or adapt to the change. The idea behind release documentation is that when a naive user with limited internal knowledge of the project is upgrading from the previous release to the new one, they should be able to read the release notes, understand if they need to update their program which uses qiskit, and how they would go about doing that. It ideally should explain why they need to make this change too, to provide the necessary context.
To make sure we don't forget a release note or if the details of user facing changes over a release cycle we require that all user facing changes include documentation at the same time as the code. To accomplish this we use the reno tool which enables a git based workflow for writing and compiling release notes.
Making a new release note is quite straightforward. Ensure that you have reno installed with::
pip install -U reno
Once you have reno installed you can make a new release note by running in your local repository checkout's root::
reno new short-description-string
where short-description-string is a brief string (with no spaces) that describes what's in the release note. This will become the prefix for the release note file. Once that is run it will create a new yaml file in releasenotes/notes. Then open that yaml file in a text editor and write the release note. The basic structure of a release note is restructured text in yaml lists under category keys. You add individual items under each category and they will be grouped automatically by release when the release notes are compiled. A single file can have as many entries in it as needed, but to avoid potential conflicts you'll want to create a new file for each pull request that has user facing changes. When you open the newly created file it will be a full template of the different categories with a description of a category as a single entry in each category. You'll want to delete all the sections you aren't using and update the contents for those you are. For example, the end result should look something like::
features:
- |
Introduced a new feature foo, that adds support for doing something to
``QuantumCircuit`` objects. It can be used by using the foo function,
for example::
from qiskit import foo
from qiskit import QuantumCircuit
foo(QuantumCircuit())
- |
The ``qiskit.QuantumCircuit`` module has a new method ``foo()``. This is
the equivalent of calling the ``qiskit.foo()`` to do something to your
QuantumCircuit. This is the equivalent of running ``qiskit.foo()`` on
your circuit, but provides the convenience of running it natively on
an object. For example::
from qiskit import QuantumCircuit
circ = QuantumCircuit()
circ.foo()
deprecations:
- |
The ``qiskit.bar`` module has been deprecated and will be removed in a
future release. Its sole function, ``foobar()`` has been superseded by the
``qiskit.foo()`` function which provides similar functionality but with
more accurate results and better performance. You should update your calls
``qiskit.bar.foobar()`` calls to ``qiskit.foo()``.You can also look at other release notes for other examples.
You can use any restructured text feature in them (code sections, tables, enumerated lists, bulleted list, etc) to express what is being changed as needed. In general you want the release notes to include as much detail as needed so that users will understand what has changed, why it changed, and how they'll have to update their code.
After you've finished writing your release notes you'll want to add the note
file to your commit with git add and commit them to your PR branch to make
sure they're included with the code in your PR.
After release notes have been added if you want to see what the full output of
the release notes. In general the output from reno that we'll get is a rst
(ReStructuredText) file that can be compiled by
sphinx. To generate the rst file you
use the reno report command. If you want to generate the full Machine Learning release
notes for all releases (since we started using reno during 0.9) you just run::
reno report
but you can also use the --version argument to view a single release (after
it has been tagged::
reno report --version 0.5.0
At release time reno report is used to generate the release notes for the
release and the output will be submitted as a pull request to the documentation
repository's release notes file
Building The release notes are part of the standard qiskit-machine-learning documentation
builds. To check what the rendered html output of the release notes will look
like for the current state of the repo you can run: tox -edocs which will
build all the documentation into docs/_build/html and the release notes in
particular will be located at docs/_build/html/release_notes.html
Please see the Installing Qiskit Machine Learning from Source section of the Qiskit documentation.
Note: Machine Learning depends on Terra, and has optional dependence on Aer and IBM Q Provider, so these should be installed too. The main branch of Machine Learning is kept working with those other element main branches so these should be installed from source too following the instructions at the same location
Machine Learning also has some other optional dependents see Machine Learning optional installs for further information. Unit tests that require any of the optional dependents will check and skip the test if not installed.
Once you've made a code change, it is important to verify that your change does not break any existing tests and that any new tests that you've added also run successfully. Before you open a new pull request for your change, you'll want to run the test suite locally.
The test suite can be run from a command line or via your IDE. You can run make test which will
run all unit tests. Another way to run the test suite is to use
tox. For more information about using tox please
refer to
Terra CONTRIBUTING
Test section. However please note Machine Learning does not have any
online tests
nor does it have
test skip
options.
The development cycle for qiskit-machine-learning is informed by release plans in the Qiskit rfcs repository
main:
The main branch is used for development of the next version of qiskit-machine-learning. It will be updated frequently and should not be considered stable. The API can and will change on main as we introduce and refine new features.
stable/*: The stable branches are used to maintain the most recent released versions of qiskit-machine-learning. It contains the versions of the code corresponding to the minor version release in the branch name release for The API on these branches are stable and the only changes merged to it are bugfixes.
From time to time, we will release brand new versions of Qiskit Machine Learning. These are well-tested versions of the software.
When the time for a new release has come, we will:
- Create a new tag with the version number and push it to github
- Change the
mainversion to the next release version.
The release automation processes will be triggered by the new tag and perform the following steps:
- Create a stable branch for the new minor version from the release tag
on the
mainbranch - Build and upload binary wheels to pypi
- Create a github release page with a generated changelog
- Generate a PR on the meta-repository to bump the terra version and meta-package version.
The stable/* branches should only receive changes in the form of bug
fixes.
In the qiskit-machine-learning repository we maintain a list of commits for git blame
to ignore. This is mostly commits that are code style changes that don't
change the functionality but just change the code formatting (for example,
when we migrated to use black for code formatting). This file,
.git-blame-ignore-revs just contains a list of commit SHA1s you can tell git
to ignore when using the git blame command. This can be done one time
with something like
git blame --ignore-revs-file .git-blame-ignore-revs qiskit_machine_learning/version.py
from the root of the repository. If you'd like to enable this by default you can update your local repository's configuration with:
git config blame.ignoreRevsFile .git-blame-ignore-revs
which will update your local repositories configuration to use the ignore list by default.