[DOC] Adding development docs to make sure it is clear how to do dev

DEVELOPING.md

@@ -1,14 +1,97 @@
+<!-- TOC -->
+
+- [Requirements](#requirements)
+- [Setting up your development environment](#setting-up-your-development-environment)
+- [Building the project from source](#building-the-project-from-source)
+- [Development Tasks](#development-tasks)
+        - [Basic Verification](#basic-verification)
+        - [Docsite](#docsite)
+    - [Details](#details)
+        - [Coding Style](#coding-style)
+        - [Lint](#lint)
+        - [Type checking](#type-checking)
+        - [Unit tests](#unit-tests)
+- [Advanced Updating submodules](#advanced-updating-submodules)
+- [Cython and C++](#cython-and-c)
+- [Making a Release](#making-a-release)
+
+<!-- /TOC -->
+
 # Requirements
-* Python 3.8+
-* Poetry (`curl -sSL https://install.python-poetry.org | python - --version=1.2.2`)
 
-For the other requirements, inspect the ``pyproject.toml`` file. If you are updated the dependencies, please run `poetry update` to update the
+* Python 3.9+
+* numpy
+* scipy
+* scikit-learn>=1.3.1
+
+For the other requirements, inspect the ``pyproject.toml`` file.
+
+# Setting up your development environment
+
+We recommend using miniconda, as python virtual environments may not setup properly compilers necessary for our compiled code. 
+
+<!-- Setup a conda env -->
+
+    conda create -n sktree python=3.9 
+    conda activate sktree
+
+**Any commands should ALWAYS be after you have activated your conda environment.**
+Next, install necessary build dependencies. For more information, see https://scikit-learn.org/stable/developers/advanced_installation.html.
+
+    conda install -c conda-forge joblib threadpoolctl pytest compilers llvm-openmp
+
+Assuming these steps have worked properly and you have read and followed any necessary scikit-learn advanced installation instructions, you can then install dependencies for scikit-tree.
+
+If you are developing locally, you will need the build dependencies to compile the Cython / C++ code:
+
+    pip install -r build_requirements.txt
+
+Other requirements can be installed as such:
+
+    pip install -r requirements.txt
+    pip install -r style_requirements.txt
+    pip install -r test_requirements.txt
+    pip install -r doc_requirements.txt
+
+# Building the project from source
+
+We leverage meson to build scikit-tree from source. We utilize a CLI tool, called [spin](https://github.com/scientific-python/spin), which wraps certain meson commands to make building easier.
+
+For example, the following command will build the project
+
+    spin build
+
+The following command will test the project
+
+    spin test
+
+For other commands, see
+
+    spin --help
+
+Note at this stage, you will be unable to run Python commands directly. For example, ``pytest ./sktree`` will not work.
+
+However, after installing and building the project from source using meson, you can leverage editable installs to make testing code changes much faster.
+
+    pip install --no-build-isolation --editable .
+
+This will now link the meson build to your Python runtime. Now if you run
+
+    pytest ./sktree
+
+the unit-tests should run.
 
 # Development Tasks
-There are a series of top-level tasks available through Poetry. These can each be run via
+There are a series of top-level tasks available through Poetry. If you are updated the dependencies, please run `poetry update` to update the lock file. These can each be run via
 
  `poetry run poe <taskname>`
 
+To do so, first install poetry and poethepoet.
+
+    pip install poetry poethepoet
+
+Now, you are ready to run quick commands to format the codebase, lint the codebase and type-check the codebase.
+
 ### Basic Verification
 * **format** - runs the suite of formatting tools applying tools to make code compliant
 * **format_check** - runs the suite of formatting tools checking for compliance
@@ -53,6 +136,23 @@ In order for any code to be added to the repository, we require unit tests to pa
 
     poetry run poe unit_test
 
+# (Advanced) Updating submodules
+
+Scikit-tree relies on a submodule of a forked-version of scikit-learn for certain Python and Cython code that extends the ``DecisionTree*`` models. Usually, if a developer is making changes, they should go over to the ``submodulev3`` branch on ``https://github.com/neurodata/scikit-learn`` and 
+submit a PR to make changes to the submodule. 
+
+This should **ALWAYS** be supported by some use-case in scikit-tree. We want the minimal amount of code-change in our forked version of scikit-learn to make it very easy to merge in upstream changes, bug fixes and features for tree-based code.
+
+Once a PR is submitted and merged, the developer can update the submodule here in scikit-tree, so that we leverage the new commit. You **must** update the submodule commit ID and also commit this change, so that way the build leverages the new submodule commit ID.
+
+    git submodule update --init --recursive --remote
+    git add -A
+    git commit -m "Update submodule" -s
+
+Now, you can re-build the project using the latest submodule changes.
+
+    spin build --clean
+
 # Cython and C++
 The general design of scikit-tree follows that of the tree-models inside scikit-learn, where tree-based models are inherently Cythonized, or written with C++. Then the actual forest (e.g. RandomForest, or ExtraForest) is just a Python API wrapper that creates an ensemble of the trees.