This project contains the source code for the following paper. Please quote it if you use the code.

Tan Nguyen, Nan Ye, Peter Bartlett. "Greedy Convex Ensemble." The 29th International Joint Conference on Artificial Intelligence (IJCAI 2020).

The code is written in Python 3. In order to run the code, PyTorch, XGBoost, Scikit-learn need to be installed. Refer to pytorch.org for how to install PyTorch. XGBoost and Scikit-learn can be installed as follows.

pip install xgboost
pip install sklearn

It is recommended to run GCE on a computer with NVIDIA GPU. We used a GTX 1080 Ti GPU card in our experiments.

Execute exp_*.py to run the experiments. For example, the following command will run all Greedy Convex Ensemble (GCE) experiments on all datasets, including hyper-parameters tuning.

python exp_gce.py 

Similarly, python exp_xgboost.py, python exp_rforest.py, python exp_nn.py, python exp_ngce.py will run experiments using XGBoost (XGB), Random Forest (RF), Neural Network (NN), Non-greedy Convex Ensemble (NGCE).

Use run_*.py to run an individual case with specific algorithm, dataset, and parameters. run_gce_nn.py is for execution of GCE, NGCE, and NN. XGB and RF use different execution scripts: run_xgboost.py and run_rforest.py. Please see the comments in these scripts for the meaning and possible values of each parameter.

The following command will train the model with default value of parameters.

python run_gce_nn.py

The following command will train the model with some parameters altered from the default value. It is possible to alter any/all parameters this way.

python run_gce_nn.py --dataset msd --algorithm pfw --model NN2 --epoch 5000 --weight-decay 0.0001 --loss l2 --activation relu

Help on the parameters of run_*.py is available using the following commands.

python run_nn_gce.py --help 
python run_xgboost.py --help
python run_rforest.py --help