machine-learning-model-local 0.0.3

PyPI Package for Circles machine-learning-model-local Python

python-package-template

This repository is designed to help you create local and remote package layers in Python.
It focuses on building package layers and does not include GraphQL and REST-API layers.

Download Environment

To set up the environment, follow these steps in your terminal:

git clone https://github.com/circles-zone/<your-repo>.git --branch dev  # or other branch
cd <your-repo>
git checkout -b BU-<new-branch>  # if a new branch is needed
python -m venv venv
pip install -r requirements.txt
... <edit your code> ...
git add .
git commit -m "Your commit message"
git push origin BU-<your-branch>

For more detailed information, refer to the documentation.

Check List:

Directory Structure

Ensure that the root directory has the following structure:

• .github/
• .vscode/ (optional)
• directory_with_same_name_as_the_repo/
  • db/
  • reports/
  • project_name/
    • src/
      • __init__.py
      • example_class.py
    • tests/
      • example_class_test.py
    • __init__.py

This setup enables easy switching to a mono repo configuration.

Database Python Scripts

Place <table-name>.py in the /db folder if needed.
There's no need for a separate file for _ml tables.
Feel free to delete the example file if it's not required.

Database Schema and Data

• Create files to define the database schema, tables, views, and populate metadata and test data.
• Use /db/<table-name>.py to create the schema, tables, views (including _ml_table).
• Use /db/<table-name>_insert.py to create metadata and test data records.

Update setup.py

Don't forget to update the setup.py file, including the package name and version.
Remember to upload the version after every deployment.

Working with VS Code

Ensure that you push the launch.json file to the repository.
This enables running and debugging the code smoothly.

Unit Testing

We recommend using pytest over the unittest package.
Create a pytest.ini file in the project directory, not the root directory. run in termianl: pytest

Workflow Completion

When you've addressed all the TODOs in the repository, using infrastructure classes like Logger, Database, Url, Importer, and others, make sure your Feature Branch GitHub Actions Workflow is green without warnings.
All tests should run in GitHub Actions, your code should be well-documented, the README.md file should be clear and self-explanatory, test coverage should be above 90%, and all lines of code should be covered by unit tests.

Once these conditions are met, you can filter and analyze your records in Logz.io.
Pull the dev branch to your Feature Branch and then create a Pull Request to dev.

Good luck :)

check your code visibility lint with flake (Mandatory before pusj):

run those command python -m pip install flake8 pytest flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics

note: you should use autopep8 extension in your code!