This repository is a template for a python data science project.

The root should contain at least the following elements:

• project/: a folder named after the project containing the code. Inside, you should follow the code organisation described below
• README.md: a markdown file describing very shortly the objective, methods, main results, and how to use the repo
• doc/: folder containing all additional and detailed documentation of your repository.
• .gitignore: list of git ignored files

Optionally, you may find a sonar-project.properties which is a configuration file for the sonarqube software, a code quality evaluator.

Inside project/, the code should be organised according to Domain Driven Design (hereafter DDD):

• infrastructure/: data collection and technical cleaning, connector to the database
• domain/: domain-related cleaning, feature engineering, machine learning algorithm, domain specific knowledge
• application/: application orchestrator or API, you may define here the order in which the cleaning processes must be run
• interface/: user interface (eg: javascript front-end). The template includes a dashboard template from ng2-admin

In addition, you will find two folders:

• tests/: unit (functions test), integration (pipeline test), acceptance (expected domain values test), scalability (workload test)
• utils/: very generic functions

• Add the repo directory to your PYTHONPATH environment variable (eg: in your .bashrc)
• Change the name of project/ directory, if necessary
• Configuration parameters (eg: location of data, API port, database configuration, etc.) should be set into a .ini file inside project/application/. You may copy project/template.ini into a my_config.ini which should not be committed.
• In order to access the configuration parameters, you can use :

from project.utils.config import Config
config = Config("template.ini")
print(config["database"]["password"]) #return 'password_test'

warning: Config returns a ConfigParser object. Please see the configparser module documentation for drawbacks (eg: an integer passed into the configuration file will be recognized as a str object)

• Import a python module using :

import project.domain.sub_directory.my_file

You don't know how to start ? Check out the python examples scripts inside each folder, starting by application/main.py. The goal of the example is to use a iris.csv dataset to make a prediction on two new rows using a random forest regressor.

Support for python3.x only