DriftAI is a Machine Learning development framework. It's main aim is to standardize the model development cycle increasing the rapidness and a higher technology flexibility, allowing the inclusion of several libraries. It establishes a common framework among developers for model reproductivity and testing.

License

Copyright (c) 2012 GFT Appverse, S.L., Sociedad Unipersonal.

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Install

1. Clone the project

$ git clone https://git.gft.com/gft-ai-tools/driftai.git && cd driftai

2. You need python and pip installed. (If don't have it download it from https://www.python.org/)

3. You need Visual C++ Build Tools. Download it from https://www.microsoft.com/es-es/download/confirmation.aspx?id=48159

4. Install OptApp with setup script.

$ python setup.py install

Using CLI to create and run a project

1. Create a project and navigate to its root

$ dai new <project_name> && cd <project_name>

2. Import a dataset from a datasource

$ dai add dataset -p <datasource_path>
Dataset with id <dataset_id> created

3. Using the dataset_id provided in the output of the dai add dataset command split your dataset into multiple sub-datasets in order to achive a statistically consistent evaluation.

$ dai generate subdataset <dataset_id> --by 5 --method k_fold
Subdataset with id <subdataset_id> created

5. Generate an approach using the subdataset_id provided in the provious output.

Tip: Approach name can't contain whitespaces.

$ dai generate approach <approach_name> --subdataset <subdataset_id>

Tip: To keep consistency and driftai automation benefits don't modify the Approach class name.

8. Fill learn, inference and parameters methods in the recently generated approach file.
   • learn: Fit your model here.
   • inference: Make predictions here.
   • parameters: Declare your hyperparameters using driftai objects (CategoricalParameter, IntParameter, FloatParameter ...)

Tip: By default approach class comes decorated with @single_run meaning that approach ig going to run in your machine (in a single computer). More decorators are being developed such as @dask_runner, @cloud_runner, etc.

9. Run your approach using

$ dai run <approach_name>

Running the examples

Inside the examples/ directory you will find a folder for each example.

To run an example navigate to its directory and run the project-setup.sh script. Also you can check the script content in order to learn how to use the CLI.

Generate the documentation

Using Sphinx CLI

$ cd docs && sphinx-build -b html source build

Using Makefile

$ cd docs && make html

Check the installation

Run all unitests using the following command:

$ python  -m unittest test/unitests/*.py