Multi-Label Feature Selection Using Adaptive and Transformed Relevance

Dependencies and Installation

1. Clone Repo

git clone https://github.com/Sadegh28/ATR.git

2. Create Conda Environment

conda create --name ATR python=3.8
conda activate ATR

3. Install Dependencies

pip install -r requirements.txt

Get Started

Mulan Datasets

Use the following command to rank features of a dataset from the mulan repository:

    python main.py   --datasets   d1, d2, ..., dn 

Each di must be a mulan dataset:

    {'Corel5k', 'bibtex', 'birds', 'delicious', 'emotions', 'enron', 'genbase', 'mediamill', 'medical',
    'rcv1subset1', 'rcv1subset2', 'rcv1subset3', 'rcv1subset4', 'rcv1subset5', 'scene', 'tmc2007_500', 'yeast'}

For example the following command ranks the features of 'emotions' and 'birds' datasets:

    python main.py   --datasets   'emotions', 'birds'

Check out the results in ./results/SelectedSubsets/

In addition, use the following command to select a subset of 20 top features (instead of ranking the entire feature space):

    python main.py   --datasets   'emotions', 'birds'     --selection-type 'fixed-num' --num-of-features 20

Your Own Dataset

1. Put your datasets into ./data folder. The folder structure for each dataset should follow the format:

    -YourDataset
    |--- train.csv
    |--- train_labels.csv
    |--- test.csv
    |--- test_labels.csv
   

2. Run the following command to rank features of a dataset from the mulan repository:

    python main.py  --data-path 'data\'  --datasets   d1, d2, ..., dn  
   

As an example, download the emotions dataset through this link. After extracting into ./data folder, you should see the follwing structure:

    -emotions
    |--- train.csv
    |--- train_labels.csv
    |--- test.csv
    |--- test_labels.csv

Now you can run the following commands for feature ranking and selection, respectively:

    python main.py  --data-path 'data\'  --datasets   'emotions'    
    python main.py  --data-path 'data\'  --datasets   'emotions'    --selection-type 'fixed-num' --num-of-features 20

pre_eval and post_eval modes

You can use 'pre_eval' and 'post_eval' modes to calculate information theoretic measures between variables. In 'pre_eval' mode, all required calculations are performed before the feature selection process. But in the case of 'post_eval', the measures are calculated on demand in the feature selection process. In general, 'pre_eval' mode runs much faster than 'post_eval' unless you want to select a very small number of features (say 5). 'pre_eval' mode is the default, and if you want to use 'post_eval' mode, run the following command:

    python main.py  --data-path 'data\'  --datasets   'emotions'  --selection-type 'fixed-num' --num-of-features 5  --eval-mode 'post_eval'