UNNT (Utility for comparing Neural Networks and Tree-based models)

UNNT enables users with structured (tabular) data to compare CNN and XGBoost models by providing a utility to train both models

Installation

UNNT requires Anaconda to run.

Create conda environment

conda env create -f gpu_environment.yml -n UNNT_gpu
conda activate UNNT_gpu

The CPU version can be installed using the environment.yml file

conda env create -f environment.yml -n UNNT
conda activate UNNT

Usage

Default run configuration trains XGBoost and CNN models with NCI60 datasets

cd UNNT
python3 unnt.py

Use --gpu when running on UNNT_GPU conda environment with a GPU

python3 unnt.py --gpu

Data

All data used to train models with UNNT must be place in the data folder at the root of this repository

The data necessary to run the in default settings using NCI60 datasets are also located in the data folder

Training with custom data

When training XGBoost and CNN using your dataset, place it in the data folder at the root of this repository and update tree_config.txt file in UNNT folder. Provide the name of the file for the configuration parameter data_file. Name of data file must be in quotes and is case sensitive. The dataset provided is required to be in csv format.

When providing custom data make sure the data is cleaned and preprocessed. The columns must be features. Provide the target variable the models train on by setting the target_variable configuration parameter in tree_config.txt

Configuration

In addition to configuration on data necessary for the entire software, tree_config.txt also contains XGBoost model parameters.

XGBoost parameters

n_estimators=500
max_depth=10
eta=0.1
subsample=0.5
colsample_bytree=0.8

CNN parameters

cnn_config.txt contains the configurations specific for CNN model. Many of these are specific to NCI60 dataset used for demo in this software and thus won't affect the models trained using custom data.

The main parameters for CNN include

dense=[1000, 500, 100, 50]
batch_size=100
epochs=1
activation='tanh'
loss='mse'
optimizer='sgd'
learning_rate=0.01
scaling='std'
dropout=0.1
feature_subsample=0
val_split=0.1
test_split=0.1

The MIT License

Distributed under the MIT License. See LICENSE for more information