Lite-AutoML:
Quick and simple AutoML framework to run on dataset for ML classification task. Evaluates Lite-AutoML in comparison to auto-sklearn, TPOT and hyperopt-sklearn.

In a conda virtual environment, install the following-
sklearn
openml
tpot

Run conda install swig

Install auto-sklearn by
curl https://raw.githubusercontent.com/automl/auto-sklearn/master/requirements.txt | xargs -n 1 -L 1 pip install

Lite-AutoML evaluates itself on OpenML datasets and the 3 frameworks
auto-sklearn
TPOT
hyperopt-sklearn

Run Lite-AutoML as-
python main.py <timeout_in_min> <output_filename> <openmlid>
where
timeout_in_min = timeout in minutes to be used by each AutoML framework

output_filename: Name of file to write scores and dataset details

Output will be written as comma-separated fields in the following format-
name,majority,classifier,id,rows,classes,autosklearn,tpot,hyperopt,liteautoml,cols,litecols

openmlid: OpenML dataset id