My personal ML sandbox. Goal: to rapidly prototype POC sk-learn models from simple, modular yaml files. To get started, create a uniquely-named project sub-directory in the projects folder.

• Add pytorch/keras model support
• AWS EC2 model training using docker

The program expects the following structure from any given project:

<project_name>
├── __init__.py
├── data
│   ├── processed
│   │   ├── X_test.txt
│   │   ├── X_train.txt
│   │   ├── X_train_val.txt
│   │   ├── X_val.txt
│   │   ├── features.txt
│   │   ├── y_test.txt
│   │   ├── y_train.txt
│   │   ├── y_train_val.txt
│   │   └── y_val.txt
└── src
    ├── __init__.py
    ├── models.yaml
    ├── prep_data.py
    └── project_settings.yaml

NOTE: Be sure to change the repo_loc in global_settings.yaml to the location of the git repository on your machine.

• prep_data.py: This is a file you write to do any custom pre-processing. It should generate all the files in the processed folder. The program will check for these files when it runs; if it doesn't find them, it will run prep_data.py.

• project_settings.yaml: see house_prices/src/project_settings.yaml as a model. This file holds several project-specific parameters/

• models.yaml: this is where most of the funcionality is driven. Again, see house_prices/src/models.yaml as an example.

All of the feature engineering/selection components that have been implemented are in the ./feature/selection.py, ./feature/engineering.py, and feature/transformchain.py files. In the class definitions are examples of usage within a models.yaml file.