Ensemble Integration (EI) is a customizable pipeline for generating diverse ensembles of heterogeneous classifiers, as well as the accompanying metadata needed for ensemble learning approaches utilizing ensemble diversity for improved performance. It also fairly evaluates the performance of several ensemble learning methods including ensemble selection [Caruana2004], and stacked generalization (stacking) [Wolpert1992]. Though other tools exist, we are unaware of a similarly modular, scalable pipeline designed for large-scale ensemble learning. EI was developed to support research by Linhua Wang and Gaurav Pandey with the support of the Icahn Institute for Genomics and Multiscale Biology at Mount Sinai.

EI is designed for generating extremely large ensembles (taking days or weeks to generate) and thus consists of an initial data generation phase tuned for multicore and distributed computing environments. The output is a set of compressed CSV files containing the class distribution produced by each classifier that serves as input to a later ensemble learning phase.

This can be done using sdkman (https://sdkman.io/).

conda create --name ei
conda install -n ei python=2.7.14 cython=0.19.1 pandas scikit-learn

curl -O -L http://prdownloads.sourceforge.net/weka/weka-3-7-10.zip

Under the data path, 2 files and a list of feature folders are expected:

1. classifiers.txt This file specifies the list of base classifiers. See the sample_data/classifiers.txt as an example.

2. weka.properties This file specifies the list of weka properties that are parsed to the training/testing of base classifiers. See the sample_data/weka.properties as an example.

3. Folders for feature sets This is a list of folders under the main data path. Each of them originally contains only one file named as data.arff. The .arff files are the input feature matrices and labels for training/testing Weka base classifiers. Indices and labels of .arff files should be aligned across all feature sets.

Arguments of train_base.py:

--path: Path to data, required.
--minerva: Specify whether use minerva or not, default is true. Following parameters work only when minerva is requested. 
	--queue: minerva queue to be used, default is premium. 
	--node: #nodes to be used, default is 20. 
	--time: walltime, default is 10:00 (10 hours).
	--memory: memory limit, default is 10240 MB.
	--classpath: path to weka file, default is ./weka.jar.

Option 1: Without access to Minerva, EI can be run sequentially.

python train_base.py --path [path] --minerva 0 

Option 2: Run the pipeline in parallel on Minerva

python train_base.py --path [path] --node [#node] --queue [queue] --time [hour:min] --memory [memory]

Arguments of ensemble.py:

--path: path to data, required.
--fold: cross validation fold, default is 5.
--agg: number to aggregate bag predictions, default is 1. 
	If each bagging classifier is treated individually, ignore it. Else if bagcount > 1, set -agg = bagcount. 

Run the follwoing command:

python ensemble.py --path P --fold fold_count --agg agg_number

F-max scores of these models will be printed and written in the performance.csv file and saved to the analysis folder under the data path.