AL

A project to automatically generate supervised learning programs by learning from previously written programs.

We collected approximately 500 programs, extracted dynamic traces, and build a model to predict the next API call based on previous j calls and features of the current data state.

We previously provided a docker demo, but have now moved on to a simple API for use. This is meant for demonstration purposes.

Build

We recommend you create a virtual environment, for example using conda.

conda create -n al-env python=3.6

You can then install the requirements for use of AL.

pushd src/core
pip install -r requirements.txt
popd

Using AL

You can import al from src/core for use.

pushd src/core
python

import al
import sklearn.datasets
X, y = sklearn.datasets.make_classification()

m = al.AL()
m.fit(X, y)
progs = m.get_programs()
print(progs[0].pipeline_code())

Note that .pipeline_code prints out a pipeline without some boilerplate (such as splitting X, y into training and validation).