You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
@@ -14,7 +36,7 @@ Read more about CheckMates on our [documentation page](#):
14
36
15
37
The CheckMates community is happy to provide support to users of CheckMates. Project support can be found in four places depending on the type of question:
16
38
1. For usage questions, use [Stack Overflow](#) with the `CheckMates` tag.
17
-
2. For bugs, issues, or feature requests start a [Github issue](#).
39
+
2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/CheckMates/issues).
18
40
3. For discussion regarding development on the core library, use [Slack](#).
19
41
4. For everything else, the core developers can be reached by email at [email protected]
Copy file name to clipboardExpand all lines: contributing.md
+9-96Lines changed: 9 additions & 96 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -1,8 +1,8 @@
1
1
## Contributing to the Codebase
2
2
3
-
#### 0. Look at Open Issues
4
-
We currently utilize GitHub Issues as our project management tool for datachecks. Please do the following:
5
-
* Look at our [open issues](#)
3
+
#### 0. Look at Open Issues
4
+
We currently utilize GitHub Issues as our project management tool for checkmates. Please do the following:
5
+
* Look at our [open issues](https://github.com/alteryx/CheckMates/issues)
6
6
* Find an unclaimed issue by looking for an empty `Assignees` field.
7
7
* If this is your first time contributing, issues labeled ``good first issue`` are a good place to start.
8
8
* If your issue is labeled `needs design` or `spike` it is recommended you provide a design document for your feature
@@ -11,19 +11,18 @@ We currently utilize GitHub Issues as our project management tool for datachecks
11
11
12
12
13
13
#### 1. Clone repo
14
-
The code is hosted on GitHub, so you will need to use Git to clone the project and make changes to the codebase. Once you have obtained a copy of the code, you should create a development environment that is separate from your existing Python environment so that you can make and test changes without compromising your own work environment. Additionally, you must make sure that the version of Python you use is at least 3.8. Using `conda` you can use `conda create -n datachecks python=3.8` and `conda activate datachecks` before the following steps.
14
+
The code is hosted on GitHub, so you will need to use Git to clone the project and make changes to the codebase. Once you have obtained a copy of the code, you should create a development environment that is separate from your existing Python environment so that you can make and test changes without compromising your own work environment. Additionally, you must make sure that the version of Python you use is at least 3.8.
15
15
* clone with `git clone [https://github.com/alteryx/CheckMates.git]`
16
16
* install in edit mode with:
17
17
```bash
18
18
# move into the repo
19
-
cddatachecks
19
+
cdcheckmates
20
20
# installs the repo in edit mode, meaning changes to any files will be picked up in python. also installs all dependencies.
21
21
make installdeps-dev
22
22
```
23
23
24
24
<!--- Note that if you're on Mac, there are a few extra steps you'll want to keep track of.
25
-
* In order to run on Mac, [LightGBM requires the OpenMP library to be installed](https://datachecks.alteryx.com/en/stable/install.html#Mac), which can be done with HomeBrew by running `brew install libomp`
26
-
* We've seen some installs get the following warning when importing datachecks: "UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError". [A known workaround](https://stackoverflow.com/a/61531555/841003) is to run `brew reinstall readline xz` before installing the python version you're using via pyenv. If you've already installed a python version in pyenv, consider deleting it and reinstalling. v3.8.2 is known to work. --->
25
+
* We've seen some installs get the following warning when importing checkmates: "UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError". [A known workaround](https://stackoverflow.com/a/61531555/841003) is to run `brew reinstall readline xz` before installing the python version you're using via pyenv. If you've already installed a python version in pyenv, consider deleting it and reinstalling. v3.9.7 is known to work. --->
27
26
28
27
#### 2. Implement your Pull Request
29
28
@@ -78,12 +77,12 @@ Note that if you're building docs locally, the warning suppression code at `docs
78
77
79
78
* We use GitHub Actions to run our PR checkin tests. On creation of the PR and forevery change you make to your PR, you'll need a maintainer to click "Approve and run" on your PR. This is a change [GitHub madein April 2021](https://github.blog/2021-04-22-github-actions-update-helping-maintainers-combat-bad-actors/).
80
79
81
-
* We ask that all contributors sign our contributor license agreement (CLA) the first time they contribute to datachecks. The CLA assistant will place a message on your PR; follow the instructions there to sign the CLA.
80
+
* We ask that all contributors sign our contributor license agreement (CLA) the first time they contribute to checkmates. The CLA assistant will place a message on your PR; follow the instructions there to sign the CLA.
82
81
83
82
Add a description of your PR to the subsection that most closely matches your contribution:
84
-
* Enhancements: new features or additions to DataChecks.
83
+
* Enhancements: new features or additions to CheckMates.
85
84
* Fixes: things like bugfixes or adding more descriptive error messages.
86
-
* Changes: modifications to an existing part of DataChecks.
85
+
* Changes: modifications to an existing part of CheckMates.
87
86
* Documentation Changes
88
87
* Testing Changes
89
88
@@ -96,92 +95,6 @@ If your work includes a [breaking change](https://en.wiktionary.org/wiki/breakin
96
95
* Description of your breaking change
97
96
```
98
97
99
-
### 4. Updating our conda package
100
-
101
-
We maintain a conda package [package](#) to give users more options of how to install datachecks.
102
-
Conda packages are created from recipes, which are yaml config files that list a package's dependencies and tests. Here is
103
-
datachecks's latest published [recipe](#).
104
-
GitHub repositories containing conda recipes are called `feedstocks`.
105
-
106
-
If you opened a PR to datachecks that modifies the packages in`dependencies` within `pyproject.toml`, or if the latest dependency bot
107
-
updates the latest version of one of our packages, you will see a CI job called `build_conda_pkg`. This section describes
108
-
what `build_conda_pkg` does and what to doif you see it fails in your pr.
109
-
110
-
#### What is build_conda_pkg?
111
-
`build_conda_pkg` clones the PR branch and builds the conda package from that branch. Since the conda build process runs our
112
-
entire suite of unit tests, `build_conda_pkg` checks that our conda package actually supports the proposed change of the PR.
113
-
We added this check to eliminate surprises. Since the conda package is released after we release to PyPi, it's possible that
114
-
we released a dependency version that is not compatible with our conda recipe. It would be a pain to try to debug this at
115
-
release-time since the PyPi release includes many possible PRs that could have introduced that change.
116
-
117
-
#### How does `build_conda_pkg` work?
118
-
`build_conda_pkg` will clone the `master` branch of the feedstock as well as you datachecks PR branch. It will
119
-
then replace the recipe in the `master` branch of the feedstock with the current
120
-
latest [recipe](#) in datachecks.
121
-
It will also modify the [source](#)
122
-
field of the local copy of the recipe and point it at the local datachecks clone of your PR branch.
123
-
This has the effect of building our conda package against your PR branch!
124
-
125
-
#### Why does `build_conda_pkg` use a recipe in datachecks as opposed to the recipe in the feedstock `master` branch?
126
-
One important fact to know about conda is that any change to the `master` branch of a feedstock will
127
-
result in a new version of the conda package being published to the world!
128
-
129
-
With this in mind, let's say your PR requires modifying our dependencies.
130
-
If we made a change to `master`, an updated version of datachecks's latest conda package would
131
-
be released. This means people who installed the latest version of datachecks prior to this PR would get different dependency versions
132
-
than those who installed datachecks after the PR got merged on GitHub. This is not desirable, especially because the PR would not get shipped
133
-
to PyPi until the next release happens. So there would also be a discrepancy between the PyPi and conda versions.
134
-
135
-
By using a recipe stored in the datachecks repo, we can keep track of the changes that need to be made for the next release without
136
-
having to publish a new conda package. Since the recipe is also "unique" to your PR, you are free to make whatever changes you
137
-
need to make without disturbing other PRs. This would not be the case if `build_conda_pkg` ran from the `master` branch of the
138
-
feedstock.
139
-
140
-
#### What to do if you see `build_conda_pkg` is red on your PR?
141
-
It depends on the kind of PR:
142
-
143
-
**Case 1: You're adding a completely new dependency**
144
-
145
-
In this case, `build_conda_pkg` is failing simply because a dependency is missing. Adding the dependency to the recipe should
146
-
make the check green. To add the dependency, modify the recipe located at `.github/meta.yaml`.
147
-
148
-
If you see that adding the dependency causes the build to fail, possibly because of conflicting versions, then iterate until
149
-
the build passes. The team will verify if your changes make sense during PR review.
150
-
151
-
**Case 2: The latest dependency bot created a PR**
152
-
If the latest dependency bot PR fails `build_conda_pkg`, it means our code doesn't support the latest version
153
-
of one of our dependencies. This means that we either have to cap the max allowed version in our requirements file
154
-
or update our code to support that version. If we opt for the former, then just like in Case 1, make the corresponding change
155
-
to the recipe located at `.github/meta.yaml`
156
-
157
-
#### What about the `check_versions` CI check?
158
-
This check verifies that the allowed versions listed in `pyproject.toml` match those listed in
159
-
the conda recipe so that the PyPi requirements and conda requirements don't get out of sync.
160
-
161
-
## Code Style Guide
162
-
163
-
* Keep things simple. Any complexity must be justified in order to pass code review.
164
-
* Be aware that while we love fancy python magic, there's usually a simpler solution which is easier to understand!
165
-
* Make PRs as small as possible! Consider breaking your large changes into separate PRs. This will make code review easier, quicker, less bug-prone and more effective.
166
-
* In the name of every branch you create, include the associated issue number if applicable.
167
-
* If new changes are added to the branch you're basing your changes off of, consider using `git rebase -i base_branch` rather than merging the base branch, to keep history clean.
168
-
* Always include a docstring for public methods and classes. Consider including docstrings for private methods too. We use the [Google docstring convention](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings), and use the [`sphinx.ext.napoleon`](https://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html) extension to parse our docstrings.
169
-
* Although not explicitly enforced by the Google convention, keep the following stylistic conventions fordocstringsin mind:
170
-
- First letter of each argument description should be capitalized.
171
-
- Docstring sentences should end in periods. This includes descriptions for each argument.
172
-
- Types should be written in lower-case. For example, use "bool" instead of "Bool".
173
-
- Always add the default value in the description of the argument, if applicable. For example, "Defaults to 1."
174
-
* Use [PascalCase (upper camel case)](https://en.wikipedia.org/wiki/Camel_case#Variations_and_synonyms) for class names, and [snake_case](https://en.wikipedia.org/wiki/Snake_case) for method and class member names.
175
-
* To distinguish private methods and class attributes from public ones, those which are private should be prefixed with an underscore
176
-
* Any code which doesn't need to be public should be private. Use `@staticmethod` and `@classmethod` where applicable, to indicate no side effects.
177
-
* Only call public methods in unit tests.
178
-
* All code must have unit test coverage. Use mocking and monkey-patching when necessary.
179
-
* Keep unit tests as fast as possible. In particular, avoid calling `fit`. Mocking can help with this.
180
-
* When you're working with code which uses a random number generator, make sure your unit tests set a random seed.
181
-
* Use `np.testing.assert_almost_equal` when comparing floating-point numbers, to avoid numerical precision issues, particularly cross-platform.
182
-
* Use `os.path` tools to keep file paths cross-platform.
183
-
* Our rule of thumb is to favor traditional inheritance over a mixin pattern.
184
-
185
98
## GitHub Issue Guide
186
99
187
100
* Make the title as short and descriptive as possible.
0 commit comments