modelFactoryPy

Python package for Model Factory created by Advanced Analytics team at KPN.

What is Model Factory?

In order to test the package with PostgreSQL, check the modelfactory docker image

How to get started with your own Model Factory:

1. First of all, you need PostgreSQL. If you do not have it and want to play with modelfactory, install PostgreSQL on your laptop or use Amazon RDS (it allows a one year free trial). ModelFactory works now also with Aster (has some limitations at the moment, see sqlalchemy_mf_aster.

2. After PostgresSQL (or Aster) is installed, create MODELFACTORY environmental variable. On Windows it can be tricky, you need to do the following:

   -add a system environment variable MODELFACTORY with value of folder of your choice, for example: C:\Projects;

   -add the following line to PATH system environment variable: %MODELFACTORY%\bin;

   -in command line call echo %MODELFACTORY% -> this should return the specified path

3. Copy the config.yaml file that you can find in the repository in folder specified in MODELFACTORY (e.g., C:\Projects). Fill in the config.yaml file with the username, password and host you use to connect to PostgreSQL/Aster.

4. Run postgres_create_tables.sql file in PostgresSQL to create correct schema and tables.

5. We are almost there. You have to install SQLAlchemy (http://pythoncentral.io/how-to-install-sqlalchemy/) and psycopg2 package. If you use Aster, you can install dialect sqlalchemy_mf_aster.

6. Install the package (by downloading or cloning it locally and calling the following from cmd: pip install -e path-to-folder-with-package

7. You should be able to run the template file without any errors (it uses the dataset titanic.csv, which is located in folder data)