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An implementation of M5 and model trees in python, compliant with scikit-learn.

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m5py

scikit-learn-compliant M5 / M5' model trees for python

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This is the readme for developers. The documentation for users is available here: https://smarie.github.io/python-m5p/

Want to contribute ?

Contributions are welcome ! Simply fork this project on github, commit your contributions, and create pull requests.

Here is a non-exhaustive list of interesting open topics: https://github.com/smarie/python-m5p/issues

nox setup

This project uses nox to define all lifecycle tasks. In order to be able to run those tasks, you should create python 3.7 environment and install the requirements:

>>> conda create -n noxenv python="3.7"
>>> activate noxenv
(noxenv) >>> pip install -r noxfile-requirements.txt

You should then be able to list all available tasks using:

>>> nox --list
Sessions defined in <path>\noxfile.py:

* tests-2.7 -> Run the test suite, including test reports generation and coverage reports.
* tests-3.5 -> Run the test suite, including test reports generation and coverage reports.
* tests-3.6 -> Run the test suite, including test reports generation and coverage reports.
* tests-3.8 -> Run the test suite, including test reports generation and coverage reports.
* tests-3.7 -> Run the test suite, including test reports generation and coverage reports.
- docs-3.7 -> Generates the doc and serves it on a local http server. Pass '-- build' to build statically instead.
- publish-3.7 -> Deploy the docs+reports on github pages. Note: this rebuilds the docs
- release-3.7 -> Create a release on github corresponding to the latest tag

Running the tests and generating the reports

This project uses pytest so running pytest at the root folder will execute all tests on current environment. However it is a bit cumbersome to manage all requirements by hand ; it is easier to use nox to run pytest on all supported python environments with the correct package requirements:

nox

Tests and coverage reports are automatically generated under ./docs/reports for one of the sessions (tests-3.7).

If you wish to execute tests on a specific environment, use explicit session names, e.g. nox -s tests-3.6.

Editing the documentation

This project uses mkdocs to generate its documentation page. Therefore building a local copy of the doc page may be done using mkdocs build -f docs/mkdocs.yml. However once again things are easier with nox. You can easily build and serve locally a version of the documentation site using:

>>> nox -s docs
nox > Running session docs-3.7
nox > Creating conda env in .nox\docs-3-7 with python=3.7
nox > [docs] Installing requirements with pip: ['mkdocs-material', 'mkdocs', 'pymdown-extensions', 'pygments']
nox > python -m pip install mkdocs-material mkdocs pymdown-extensions pygments
nox > mkdocs serve -f ./docs/mkdocs.yml
INFO    -  Building documentation...
INFO    -  Cleaning site directory
INFO    -  The following pages exist in the docs directory, but are not included in the "nav" configuration:
  - long_description.md
INFO    -  Documentation built in 1.07 seconds
INFO    -  Serving on http://127.0.0.1:8000
INFO    -  Start watching changes
...

While this is running, you can edit the files under ./docs/ and browse the automatically refreshed documentation at the local http://127.0.0.1:8000 page.

Once you are done, simply hit <CTRL+C> to stop the session.

Publishing the documentation (including tests and coverage reports) is done automatically by the continuous integration engine, using the nox -s publish session, this is not needed for local development.

Packaging

This project uses setuptools_scm to synchronise the version number. Therefore the following command should be used for development snapshots as well as official releases: python setup.py sdist bdist_wheel. However this is not generally needed since the continuous integration engine does it automatically for us on git tags. For reference, this is done in the nox -s release session.

Merging pull requests with edits - memo

Ax explained in github ('get commandline instructions'):

git checkout -b <git_name>-<feature_branch> main
git pull https://github.com/<git_name>/python-m5p.git <feature_branch> --no-commit --ff-only

if the second step does not work, do a normal auto-merge (do not use rebase!):

git pull https://github.com/<git_name>/python-m5p.git <feature_branch> --no-commit

Finally review the changes, possibly perform some modifications, and commit.

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An implementation of M5 and model trees in python, compliant with scikit-learn.

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