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.
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
Some people have had issues using the install suggested above,
particularly they encounter issues relating to xgboost. To address this,
users can instead build a docker container for AL. We refer the
user to https://www.docker.com/ for information on how to install docker.
Once docker is installed, the container can be built by running
docker build . -t al-container
And it can be launched by running
docker run -it -rm al-container
which launches a (temporary) container with bash as an entry point.
You can then launch the python interpreter and import AL following
the steps in the next subsection.
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).