Lifcyecle manegement development notes

README.md

@@ -10,7 +10,7 @@ Warning:
 > The current version only supports Linux.
 
 
-PyMurTree is a Python wrapper for the [MurTree project](https://github.com/DCC/murtree). The MurTree algorithm constructs optimal classification trees that minimize the misclassification score of a given dataset while respecting constraints on depth and number of feature nodes. The sparse objective, which penalizes each node added in the tree, is also supported.
+PyMurTree is a Python wrapper for the [MurTree project](https://github.com/MurTree/murtree). The MurTree algorithm constructs optimal classification trees that minimize the misclassification score of a given dataset while respecting constraints on depth and number of feature nodes. The sparse objective, which penalizes each node added in the tree, is also supported.
 
 ---
 ## **Citation**

docs/index.rst

@@ -18,6 +18,7 @@ Welcome to PyMurTree's documentation!
    :caption: For developers
 
    api_reference
+   lifecycle.md
 
 
 Indices and tables

docs/lifecycle.md

@@ -0,0 +1,24 @@
+# Lifecycle & automation
+All the CI automation and lifecycle management is done through GitHub Actions. 
+There are different rules for different workflows.
+
+## Development and branching
+Even though we are not strictly using [git-flow](https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow#:~:text=Gitflow%20is%20an%20alternative%20Git,lived%20branches%20and%20larger%20commits.), we are following a similar branching strategy.
+- All features are developed in feature branches. `git checkout -b feature/my_feature`
+- All features are merged into the `develop` branch. `git checkout develop && git merge feature/my_feature`
+- All bugfixes are developed in bugfix branches. `git checkout -b bugfix/my_bugfix`
+
+### Forking and Pull requests
+- Promote forking of the repository for better collaboration. This is a good practice for open source projects. This will allow you also to have control over rights, permissions and actions environments.
+- This also allows you to have a separate repository for a contributors own development and testing. Contributers can then make a PR to the main repository when you are ready.
+- Contributing from forks can only be made through Pull requests, this makes the overall codebase management more transparent.
+- Furthermore there are specific automations like tests that are triggered on pull requests. This is a good way to make sure that the code is tested before merging, and educates contributors in writing tests.
+
+## What is automated?
+- Tests on every pull request to develop and main branches for all platforms (Linux, Windows, MacOS). Worfklow: [`pip.yaml`](../.github/workflows/pip.yml).
+- Automatic build and distribution to pypi on every release. Workflow: [`pypi.yaml`](../.github/workflows/wheels.yml).
+- Automatic documentation generation. Workflow: [`docs.yaml`](../.github/workflows/docs.yaml).
+
+## Releasing
+- Update the version number in `pyproject.toml`. Do the same for the citation file. `CITAION.cff``.
+- Use the release tag in GitHub to create a new release. This will trigger the automatic build and distribution to pypi. This manual step will trigger the workflow: [`pypi.yaml`](../.github/workflows/wheels.yaml).

docs/readme.md

@@ -66,26 +66,7 @@ ctest
 
 ## Usage
 
-### API
-
-The full [API specification](https://github.com/MurTree/pymurtree/wiki/API-documentation) is available in the repo's Wiki. 
-
-pymurtree is implemented as a thin Python wrapper around the main C++ MurTree application. The main functionality of MurTree is exposed in pymurtree via the OptimalDecisionTreeClassifier class. Utility functions to load training datasets and export the tree in text and dot formats are also included in the python package.
-
-**OptimalDecisionTreeClassifier class**
-- `constructor`: initialize the parameters of the model  
-- `fit`: fit a decision tree classifier to the given training dataset
-- `predict`: predict the labels for a set of features
-- `score`: return the accuracy on the given test data and labels
-- `depth`: return the depth of the tree
-- `num_nodes`: return the number of nodes of the tree
-- `export_text`: export decision tree in text format
-- `export_dot`: export decision tree in DOT format
-
-**Utility functions**
-- `read_from_file`: read features and labels from file into a pandas dataframe
-- `load_data`: read features and labels from file into a numpy array
-
+The full [API specification](https://murtree.github.io/pymurtree/api_reference.html) is available in the repo's documentation. 
 
 ### Example